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Ensemble methods combine multiple models to improve predictive performance from "summary" of Machine Learning by Stephen Marsland
Ensemble methods take a different approach to machine learning by combining multiple models to enhance predictive performance. This approach is based on the idea that a group of models working together can outperform any individual model. In ensemble methods, each model in the group is trained independently on the same dataset, but with different parameters or using different algorithms. There are different types of ensemble methods, including bagging, boosting, and stacking. Bagging involves training multiple models in parallel and then averaging their predictions to make the final prediction. Boosting, on the other hand, focuses on sequentially training models, where each subsequent model corrects the errors of the previous ones. Stacking combines the predictions of multiple models using another model, known as a meta-model, to make the final prediction. One of the main advantages of ensemble methods is that they can reduce overfitting by combining the strengths of different models while mitigating their weaknesses. By averaging out the predictions of multiple models, ensemble methods can produce more stable and reliable predictions. Additionally, ensemble methods are often more robust to noise and outliers in the data, leading to improved generalization performance.- Such as classification, regression, and clustering. They have been used in diverse fields, including finance, healthcare, and natural language processing, to enhance predictive performance and achieve state-of-the-art results. Overall, ensemble methods provide a powerful and effective approach to improving the accuracy and robustness of machine learning models by leveraging the collective knowledge of multiple models.
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